Predictive Analytics and Machine Learning in Direct Marketing for Anticipating Bank Term Deposit Subscriptions

Ahmed Mohamed Zaki¹ , Nima Khodadadi², Wei Hong Lim³, S.K.Towfek^1*

¹ Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA

² Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA

³ Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia

Emails: azaki@jcsis.org; nima.khodadadi@miami.edu ; limwh@ucsiuniverisity.edu.my; sktowfek@jcsis.org

Abstract

Direct marketing strategies in the banking sector have undergone evolution with the integration of predictive analytics and machine learning techniques. The focus of this study is on the utilization of these technologies to foresee bank term deposit subscriptions. The methodology encompasses data exploration, visualization, and the implementation of machine learning models. Datasets from Kaggle are employed, relationships within the data are explored through crosstabulations and heat maps, and feature engineering and preprocessing techniques are applied. The study individually implements models such as SGD Classifier, k-nearest neighbor Classifier, and Random Forest Classifier. The results indicate that the best performance among the evaluated models was exhibited by the Random Forest Classifier, achieving an accuracy of 87.5%, a negative predictive value (NPV) of 92.9972%, and a positive predictive value (PPV) of 87.8307%. These findings provide valuable insights for banks seeking to optimize their marketing strategies within the dynamic landscape of the financial industry.

Keywords: Direct Marketing; Predictive Analytics; Machine Learning; Bank Term Deposit Subscriptions; Data Exploration; Feature Engineering.